• Proceedings of the Korean Information Science Society Conference
• /
• 2012.06b
• /
• pp.135-137
• /
• 2012

Safety Critical 시스템은 예상치 못한 오류가 발생했을 시 인명, 재산 및 심각한 환경 피해 등이 발생 할 수 있는 임베디드 시스템이다. Safety Critical 시스템에는 많은 위험성들이 잠재적으로 존재하기 때문에 치명적인 사고가 발생할 수 있다. 이러한 문제를 방지하기 위해 시스템에 존재하는 위험성을 분석하는 활동이 중요시 되고 있다. 본 연구에서는 Safety Critical 시스템의 잠재적인 위험성들을 분석하고 이를 아키텍처 설계에 반영함으로써 시스템의 안전성을 향상시키기 위한 Hazard Perspective를 제안한다. 제안하는 Hazard Perspective는 위험성 분석으로 산출된 정보들과 SSR(System Safety Requirement)을 시스템의 안전을 보장하기 위해 제시된 여러 Safety Architectural Tactics와 매핑하여 아키텍처 설계에 반영시킴으로써 시스템의 안전성을 향상시키도록 한다.

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.8
• /
• pp.391-400
• /
• 2017

Software architecture plays a key role in satisfying non-functional requirement (NFR), i.e. quality requirements and constraints. Architecture design methods and tactics for conventional NFR are largely available in literatures. However, the methods for the target system-specific non-conventional NFRs are not readily available; rather architects should invent the design methods from their experiences and intuitions. Hence, the hardship to design architectures for non-conventional NFRs is quite high. In this paper, we provide a systematic architecture design methodology for non-conventional NFRs. We provide a five-step process, and detailed instructions for the steps. In the process, we treat the traceability among artifacts and seamlessness as essential values for supporting effective architecture design. We apply the methodology on designing architectures for a platform software system. We believe that the proposed methodology can be effectively utilized in designing high quality architectures for non-conventional NFRs.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.7
• /
• pp.295-302
• /
• 2023

Software requirements written in natural language may have different meanings from the stakeholders' viewpoint. When designing an architecture based on quality attributes, it is necessary to accurately classify quality attribute requirements because the efficient design is possible only when appropriate architectural tactics for each quality attribute are selected. As a result, although many natural language processing models have been studied for the classification of requirements, which is a high-cost task, few topics improve classification performance with the imbalanced quality attribute datasets. In this study, we first show that the classification model can automatically classify the Korean requirement dataset through experiments. Based on these results, we explain that data augmentation through EDA(Easy Data Augmentation) techniques and undersampling strategies can improve the imbalance of quality attribute datasets, and show that they are effective in classifying requirements. The results improved by 5.24%p on F1-score, indicating that handling imbalanced data helps classify Korean requirements of classification models. Furthermore, detailed experiments of EDA illustrate operations that help improve classification performance.